U.S. patent application number 12/763838 was filed with the patent office on 2010-11-25 for image processing apparatus, image processing method, and storage medium.
This patent application is currently assigned to CANON KABUSHIKI KAISHA. Invention is credited to Tomokazu Yanai.
Application Number | 20100296751 12/763838 |
Document ID | / |
Family ID | 43124603 |
Filed Date | 2010-11-25 |
United States Patent
Application |
20100296751 |
Kind Code |
A1 |
Yanai; Tomokazu |
November 25, 2010 |
IMAGE PROCESSING APPARATUS, IMAGE PROCESSING METHOD, AND STORAGE
MEDIUM
Abstract
An image processing apparatus comprising: a calculation unit
configured to calculate, based on a pixel value of a pixel of
interest and a pixel value of a pixel around the pixel of interest,
a spatial frequency around the pixel of interest and a statistic
value of pixel values around the pixel of interest; a storage unit
configured to store correction parameters corresponding to sets of
spatial frequencies and statistic values; an acquisition unit
configured to acquire, from the storage unit, a correction
parameter corresponding to the spatial frequency around the pixel
of interest and the statistic value of the pixel values around the
pixel of interest, as a correction parameter for the pixel of
interest; and a correction unit configured to correct the pixel
value of the pixel of interest using the correction parameter for
the pixel of interest.
Inventors: |
Yanai; Tomokazu;
(Yokohama-shi, JP) |
Correspondence
Address: |
FITZPATRICK CELLA HARPER & SCINTO
1290 Avenue of the Americas
NEW YORK
NY
10104-3800
US
|
Assignee: |
CANON KABUSHIKI KAISHA
Tokyo
JP
|
Family ID: |
43124603 |
Appl. No.: |
12/763838 |
Filed: |
April 20, 2010 |
Current U.S.
Class: |
382/274 |
Current CPC
Class: |
H04N 1/6019
20130101 |
Class at
Publication: |
382/274 |
International
Class: |
G06K 9/40 20060101
G06K009/40 |
Foreign Application Data
Date |
Code |
Application Number |
May 22, 2009 |
JP |
2009-124721 |
Claims
1. An image processing apparatus comprising: a calculation unit
configured to calculate, based on a pixel value of a pixel of
interest and a pixel value of a pixel around the pixel of interest,
a spatial frequency around the pixel of interest and a statistic
value of pixel values around the pixel of interest; a storage unit
configured to store correction parameters corresponding to sets of
spatial frequencies and statistic values; an acquisition unit
configured to acquire, from said storage unit, a correction
parameter corresponding to the spatial frequency around the pixel
of interest and the statistic value of the pixel values around the
pixel of interest, as a correction parameter for the pixel of
interest; and a correction unit configured to correct the pixel
value of the pixel of interest using the correction parameter for
the pixel of interest.
2. The apparatus according to claim 1, wherein said acquisition
unit estimates the correction parameter for the pixel of interest
using the correction parameters held by said storage unit.
3. The apparatus according to claim 1, wherein a pixel value
difference before and after correction by said correction unit
becomes large as the spatial frequency around the pixel of interest
becomes high.
4. The apparatus according to claim 1, wherein the statistic value
includes at least one of a maximum value of the pixel values, a
minimum value of the pixel values, a median of the pixel values, a
mode of the pixel values, an average value of the pixel values, a
difference between the maximum value of the pixel values and the
minimum value of the pixel values, a contrast, and a frequency
direction.
5. An image processing apparatus for generating the correction
parameters held by the storage unit of claim 1, comprising: a unit
configured to acquire an image x with a frequency x and an image y
with a frequency y (y>x); a unit configured to acquire feature
amounts of the image y including a frequency of the image y and a
statistic value of pixel values of the image y; a unit configured
to acquire a luminance value x of an output image obtained by
causing an output device to output the image x, and acquire a
luminance value y of an output image obtained by causing the output
device to output the image y; a set value acquisition unit
configured to acquire a set value preset for a variation of the
luminance value between the image x and the image y; a correction
parameter calculation unit configured to calculate a correction
parameter that reduces a difference between the set value and the
variation between the luminance value x and the luminance value y
of the output image, as a correction parameter to be used to
correct a luminance value of each pixel included in a region of an
image corresponding to the feature amounts of the image y before
giving the image to the output device; and a unit configured to
register, in a memory, a set of the feature amounts of the image y
and the correction parameter calculated by said correction
parameter calculation unit.
6. The apparatus according to claim 5, wherein said correction
parameter calculation unit comprises: a unit configured to acquire
a value obtained by dividing the luminance value y by the luminance
value x as a variation between the luminance value x and the
luminance value y; and a unit configured to calculate, as the
correction parameter, a value obtained by dividing the set value by
the variation between the luminance value x and the luminance value
y.
7. The apparatus according to claim 6, wherein the apparatus
generates the correction parameter to make the output image of the
output device match with an output image of a specific device, and
letting D_m be an average distance between an observer and the
output image of the output device, D_t be an average distance
between the observer and the output image of the specific device,
and dRatio be (D_t/D_m), when the luminance value of the output
image obtained by causing the specific device to output an image
whose frequency is (frequency y.times.dRatio) is a target luminance
value y, and the luminance value of the output image obtained by
causing the specific device to output an image whose frequency is
(frequency x.times.dRatio) is a target luminance value x, said set
value acquisition unit acquires, as the set value, a value obtained
by dividing the target luminance value y by the target luminance
value x.
8. The apparatus according to claim 7, wherein said set value
acquisition unit acquires a set value preset for a variation of a
luminance value between the image x and an image y1 with a
frequency y1 as a set value y1 for the frequency y1, acquires a set
value preset for a variation of a luminance value between the image
x and an image y2 with frequency y2 as a set value y2 for the
frequency y2, and obtains a set value for the image y with the
frequency y by linear interpolation using the set value y1 and the
set value y2.
9. The apparatus according to claim 5, wherein the frequency x is
zero.
10. The apparatus according to claim 5, wherein the correction
parameters are obtained for a plurality of images y with different
frequencies.
11. An image processing method comprising: a calculation step of
calculating, based on a pixel value of a pixel of interest and a
pixel value of a pixel around the pixel of interest, a spatial
frequency around the pixel of interest, and a statistic value of
pixel values around the pixel of interest; an acquisition step of
acquiring, from a storage unit configured to store correction
parameters corresponding to sets of spatial frequencies and
statistic values, a correction parameter corresponding to the
spatial frequency around the pixel of interest and the statistic
value of the pixel values around the pixel of interest, as a
correction parameter for the pixel of interest; and a correction
step of correcting the pixel value of the pixel of interest using
the correction parameter for the pixel of interest.
12. An image processing method of generating the correction
parameters of claim 11, comprising: a step of acquiring an image x
with a frequency x and an image y with a frequency y (y>x); a
step of acquiring feature amounts of the image y including a
frequency of the image y and a statistic value of pixel values of
the image y; a step of acquiring a luminance value x of an output
image obtained by causing an output device to output the image x,
and acquiring a luminance value y of an output image obtained by
causing the output device to output the image y; a set value
acquisition step of acquiring a set value preset for a variation of
the luminance value between the image x and the image y; a
correction parameter calculation step of calculating a correction
parameter that reduces a difference between the set value and the
variation between the luminance value x and the luminance value y
of the output image, as a correction parameter to be used to
correct a luminance value of each pixel included in a region of an
image corresponding to the feature amounts of the image y before
giving the image to the output device; and a step of registering,
in a memory, a set of the feature amounts of the image y and the
correction parameter calculated in the correction parameter
calculation step.
13. A storage medium storing a computer program which causes a
computer to function as units of an image processing apparatus of
claim 1.
Description
BACKGROUND OF THE INVENTION
[0001] 1. Field of the Invention
[0002] The present invention relates to a technique to correct a
color output from a device.
[0003] 2. Description of the Related Art
[0004] Image output devices are roughly classified into intensity
modulation (density modulation) type output devices and dot area
modulation type output devices. There are various intensity
modulation type output devices. Typical examples are a CRT, liquid
crystal display, and sublimation printer compatible with multi-bit
output. On the other hand, there are various dot area modulation
type output devices. Typical examples are an inkjet printer and an
electrophotographic printer.
[0005] The intensity modulation type output device expresses
grayscale by the luminance level of each pixel. In the output of
the intensity modulation type output device, each pixel does not
overlap an adjacent pixel. The intensity modulation type output
device is designed not to easily change the size of a pixel
depending on the luminance level. As is known, if adjacent pixels
overlap, or the size of a pixel changes in accordance with the
luminance, the luminance characteristic becomes nonlinear. In the
intensity modulation type output device, if the average pixel value
of image data is the same, the output luminance characteristic
hardly changes even when the luminance pattern (spatial frequency
characteristic) has changed.
[0006] On the other hand, the dot area modulation type output
device expresses grayscale by the area of each pixel. Especially in
an inkjet printer or electrophotographic printer, an ink dot or
toner dot corresponding to a pixel is designed to be relatively
large with respect to the output resolution. Dots that are designed
relatively large readily overlap. As is known, if the dots overlap,
the luminance characteristic generally becomes nonlinear. Hence, in
principle, in the inkjet printer or electrophotographic printer,
even if the average pixel value of image data is the same, the
output luminance characteristic readily changes when the dot
pattern (spatial frequency characteristic) has changed.
[0007] Recently, a correction technique to match the color of a dot
area modulation type output device with that of an intensity
modulation type output device (color matching) has been proposed. A
correction technique of matching the color of an intensity
modulation type output device with that of a dot area modulation
type output device and a correction technique of matching the color
of a dot area modulation type output device with an ideal color
have also been proposed. For example, Japanese Patent Laid-Open No.
7-333822 discloses a technique of performing color correction of
the above-described dot area modulation type output device such as
a printer. According to the invention described in Japanese Patent
Laid-Open No. 7-333822, when printing using a plurality of kinds of
dots with different print characteristics, the tone characteristic
of an image signal is corrected in accordance with the dot gain
characteristic.
[0008] According to the invention described in Japanese Patent
Laid-Open No. 7-333822, a high color correction accuracy can be
obtained for an image region whose spatial frequency is almost
zero. However, when an image contains high-frequency components and
has a changeable pixel value distribution (for example, contrast,
average value, and histogram), the color correction accuracy
degrades.
[0009] For example, FIG. 1A illustrates images output by a liquid
crystal display that is an intensity modulation type output device.
In FIG. 1A, reference numerals 1001 and 1002 denote input images
each having different spatial frequencies in the X direction. In
the example of FIG. 1A, the spatial frequency of the input image
1001 is 12.5 dpi, and the spatial frequency of the input image 1002
is 100 dpi. FIG. 1B shows average luminances 1101 and 1102 at this
time. As is apparent from FIG. 1B, even when the spatial
frequencies are different, the average luminance is not different.
In FIG. 1A, reference numerals 1003 and 1004 denote input images
each having different spatial frequencies in the Y direction. The
spatial frequency of the input image 1003 is 12.5 dpi, like the
input image 1001. The spatial frequency of the input image 1004 is
100 dpi, like the input image 1002. As is apparent from FIG. 1B,
even when the spatial frequencies are different, the average
luminance is not different in this case as well.
[0010] FIG. 2A illustrates images output by an inkjet printer that
is a dot area modulation type output device. The images shown in
FIG. 2A use the error diffusion method as halftone processing. As
the error diffusion method, conventional processing using an
appropriate y table is performed. In FIG. 2A, reference numerals
1201 and 1202 denote input images each having different spatial
frequencies in the main scanning direction (X direction), as in
FIG. 1A. In the example of FIG. 2A, the spatial frequency of the
input image 1201 is 18.75 dpi. The spatial frequency of the input
image 1202 is 600 dpi. FIG. 2B shows average luminances 1301 and
1302 in this case. As is apparent from FIG. 2B, when the spatial
frequencies of input images are different, the average luminances
are also different. In addition, if the input image has a high
frequency, the output image becomes dark.
[0011] In FIG. 2A, reference numerals 1203 and 1204 denote input
images each having different spatial frequencies in the
sub-scanning direction (Y direction). FIG. 2B shows average
luminances 1303 and 1304 in this case. As is apparent from FIG. 2B,
when the spatial frequencies of input images are different, the
average luminances are also different, and if the input image has a
high frequency, the output image becomes dark even in the
sub-scanning direction (Y direction). Furthermore, the luminance
characteristic is different from that in the main scanning
direction (X direction), as can be seen.
[0012] A possible cause of the above problems is that the .gamma.
table used in the above-described technique is generated based on
data obtained by measuring a plurality of color patches by a
colorimeter or the like. In other words, a possible cause is that
the correction is suitable for a region whose spatial frequency is
almost zero. As a result, when the spatial frequency, frequency
direction, and pixel value distribution (for example, contrast,
average value, histogram, minimum value, and maximum value) of an
input image change, the color correction accuracy degrades.
SUMMARY OF THE INVENTION
[0013] The present invention has been made to solve the
above-described problems, and can provide a method of improving the
accuracy of color correction performed when outputting input image
data having spatial frequency components.
[0014] According to one aspect of the invention, an image
processing apparatus comprises: a calculation unit configured to
calculate, based on a pixel value of a pixel of interest and a
pixel value of a pixel around the pixel of interest, a spatial
frequency around the pixel of interest and a statistic value of
pixel values around the pixel of interest; a storage unit
configured to store correction parameters corresponding to sets of
spatial frequencies and statistic values; an acquisition unit
configured to acquire, from the storage unit, a correction
parameter corresponding to the spatial frequency around the pixel
of interest and the statistic value of the pixel values around the
pixel of interest, as a correction parameter for the pixel of
interest; and a correction unit configured to correct the pixel
value of the pixel of interest using the correction parameter for
the pixel of interest.
[0015] Further features of the present invention will become
apparent from the following description of exemplary embodiments
(with reference to the attached drawings).
BRIEF DESCRIPTION OF THE DRAWINGS
[0016] FIGS. 1A and 1B illustrate outputs of a liquid crystal
display that is an intensity modulation type output device;
[0017] FIGS. 2A and 2B illustrate outputs of an inkjet printer that
is a dot area modulation type output device;
[0018] FIG. 3 is a block diagram showing the electrical arrangement
of an image processing apparatus according to the second
embodiment;
[0019] FIGS. 4A and 4B are views showing examples of filters to be
used to calculate an average pixel value in step S402;
[0020] FIG. 5 is a block diagram showing the arrangement of an
image processing apparatus according to the first embodiment;
[0021] FIG. 6 is a flowchart illustrating processing in a output
measurement mode;
[0022] FIGS. 7A to 7E are views showing examples of images used in
the output measurement mode;
[0023] FIG. 8 is a block diagram showing the arrangement of an
image processing unit 13;
[0024] FIG. 9 is a flowchart illustrating details of image
processing in step S103;
[0025] FIGS. 10A to 10C are schematic views showing binarization in
step S204;
[0026] FIG. 11 is a schematic view showing the relationship between
input image data and output image data;
[0027] FIGS. 12A and 12B are tables showing examples of data
obtained by processing of obtaining a measured reduction rate;
[0028] FIGS. 13A and 13B are tables showing the formats of target
data;
[0029] FIG. 14 is a schematic view showing the positional
relationship between an observer, a correction output, and a target
device;
[0030] FIGS. 15A and 15B are tables showing examples of correction
parameters calculated by a correction parameter calculation unit
42;
[0031] FIG. 16 is a flowchart illustrating processing in a output
correction mode;
[0032] FIG. 17 is a flowchart illustrating details of image
processing in step S303;
[0033] FIGS. 18A to 18D are views showing examples of filters to be
used to calculate a frequency in step S402; and
[0034] FIG. 19 is a graph showing the characteristics of filters
used in step S402.
DESCRIPTION OF THE EMBODIMENTS
[0035] Preferred embodiments will now be described with reference
to the accompanying drawings.
First Embodiment
[0036] FIG. 5 is a block diagram showing the arrangement of an
image processing apparatus according to this embodiment. Referring
to FIG. 5, reference numeral 1 denotes an image correction device;
2, an output device; 3, a measuring device; and 4, a correction
parameter calculation device.
[0037] The image correction device 1 can be implemented by, for
example, a printer driver installed in a general personal computer.
In that case, the units of the image correction device 1 to be
described below are implemented by causing the computer to execute
a predetermined program. This will be described later in detail. As
another arrangement, for example, the output device 2 may include
the image correction device 1.
[0038] The outline of the image processing apparatus according to
this embodiment will be explained below. In the image correction
device 1, first, an output mode setting unit 11 acquires a user
instruction, and selects an output mode in accordance with the user
instruction. More specifically, the output mode setting unit 11
selects a "output measurement mode" or a "output correction mode".
Processes of the devices according to the embodiment change between
the "output measurement mode" and the "output correction mode".
Details will be described later. An image data acquisition unit 12
acquires and stores image data.
[0039] When the output mode selected by the output mode setting
unit 11 is the "output measurement mode", an image processing unit
13 processes reference image data without using correction
parameters held by a correction parameter-storage unit 14. If the
output mode is the "output correction mode", the image processing
unit 13 processes the image data held by the image data acquisition
unit 12 using the correction parameters in the correction
parameter-storage unit 14, thereby generating corrected image
data.
[0040] In the output device 2, a processed image data storage unit
21 acquires and stores the corrected image data. An output unit 22
outputs the corrected image data stored in the processed image data
storage unit 21. In the measuring device 3, a measured data
acquisition unit 31 measures the image output from the output unit
22, and acquires measured data.
[0041] In the correction parameter calculation device 4, a target
and measured data storing unit 41 acquires and stores the measured
data measured by the measured data acquisition unit 31. The target
and measured data storing unit 41 also stores target data (set
value) representing an ideal output. The target data may be held in
a target data storage unit 43 (not shown) in advance.
Alternatively, the target data storage unit 43 may acquire the
target data when the reference image data has been acquired.
Details of the target data will be described later. A correction
parameter calculation unit 42 calculates correction parameters from
the target data and measured data stored in the target and measured
data storing unit 41. The correction parameters are used to correct
the luminance value of each pixel constituting a region with a
specific feature amount in the image before sending it to the
output device (output unit 22). The correction parameter
calculation unit 42 also transfers the calculated correction
parameters to the correction parameter-storage unit 14 of the image
correction device 1. The correction parameter-storage unit 14
stores the transferred correction parameters. For example, the
correction parameter-storage unit 14 stores the correction
parameters in its internal memory.
[0042] [Output Measurement]
[0043] The "output measurement mode" of the image processing
apparatus according to this embodiment having the above-described
functions will be described next with reference to the flowchart of
FIG. 6. First, the output mode setting unit 11 acquires a user
instruction, and sets the output mode to the output measurement
mode (step S101).
[0044] Next, the image data acquisition unit 12 acquires and stores
image data as reference image data (step S102). The reference image
data acquired in the output measurement mode has known feature
amounts (for example, spatial frequency, direction, and pixel value
distribution), as shown in FIG. 7. In this embodiment, the spatial
frequency, frequency direction, average pixel value, and contrast
are used as the feature amounts. The feature amounts will be
described later in detail. The target data storage unit 43 may
acquire the known feature amounts in step S102. The known feature
amounts may be held in the target data storage unit 43 in advance.
In this case, the image data acquisition unit 12 may acquire
reference image data corresponding to the feature amounts held in
the target data storage unit 43.
[0045] For example, in an image 1401 shown in FIG. 7A, the spatial
frequency in the Y direction is 0.369 [cycle/mm], the average pixel
value is 128, and the contrast is 255. The image data acquisition
unit 12 also acquires images with known feature amounts, which are
obtained by changing the feature amounts, frequency direction, and
the like of the image. For example, the image data acquisition unit
12 acquires an image 1402 in FIG. 7B obtained by changing the
spatial frequency, an image 1403 in FIG. 7C obtained by changing
the contrast, an image 1404 in FIG. 7D obtained by changing the
average pixel value, an image 1405 in FIG. 7E obtained by changing
the frequency direction, and the like.
[0046] Next, the image processing unit 13 processes the reference
image data acquired by the image data acquisition unit 12 (step
S103). A detailed operation of the image processing unit 13
according to this embodiment in the output measurement mode will be
described below with reference to the block diagram of FIG. 8 and
the flowchart of FIG. 9.
[0047] FIG. 8 shows the units of the image processing unit 13. The
output mode setting unit 11 sets a switching unit 131 based on the
setting acquired from the user (step S201). Note that if the
switching unit 131 is set to the output measurement mode, a color
conversion unit 132 acquires image data directly without making a
feature amount calculation unit 138 and a correction unit 139
intervene. If the switching unit 131 is set to the output
correction mode, the feature amount calculation unit 138 acquires
image data, and sends the data to the color conversion unit 132 via
the correction unit 139. Since the output measurement mode is set
here, the operations of the feature amount calculation unit 138 and
the correction unit 139 will not be described.
[0048] The color conversion unit 132 then converts the image data
acquired as R, G, and B data into R', G', and B' data by looking up
a color conversion LUT 133 (step S202).
R'=R_LUT3D(R,G,B) (1)
G'=G_LUT3D (R,G,B) (2)
B'=B_LUT3D (R,G,B) (3)
[0049] The functions defined on the right-hand sides of equations
(1) to (3) correspond to the color conversion LUT 133. Color
conversion of this embodiment is thus completed.
[0050] Next, a color separation unit 134 converts the R', G', and
B' data generated by the color conversion unit 132 into C, M, Y,
and K data by looking up a color separation LUT 135 (step
S203).
=C_LUT3D (R',G',B') (4)
M=M_LUT3D (R',G',B') (5)
Y=Y_LUT3D (R',G',B') (6)
K=K_LUT3D (R',G',B') (7)
[0051] The functions defined on the right-hand sides of equations
(4) to (7) correspond to the color separation LUT 135. The color
separation LUT 135 defines the method of converting three input
values corresponding to red, green, and blue into output values
corresponding to the ink colors. In this embodiment, since the four
colors, in other words, C, M, Y, and K are used as outputs, the
color separation unit 134 obtains four output values from three
input values. However, the outputs are not limited to the four C,
M, Y, and K colors. For example, another combination of ink colors
such as (C, M, Y, K, Lc, and Lm) or (C, M, Y, K, R, G, and B) is
also usable. Color separation of this embodiment is thus
completed.
[0052] Using a halftone LUT 137, a halftone processing unit 136
converts the data generated by the color separation unit 134 into
binary data (step S204). In this embodiment, the halftone
processing unit 136 performs binarization by dither processing.
However, the halftone processing unit 136 may use a binarization
method such as the error diffusion method. For example, FIG. 10A
shows data C 1501 before binarization, FIG. 10B shows data Cb 1502
after binarization, and FIG. 10C shows a threshold matrix 1503. The
threshold matrix 1503 represents the operation performed by the
halftone LUT.
[0053] More specifically, in the dither processing of the C, M, Y,
and K colors, let Th_C, Th_M, Th_Y, and Th_K be the threshold
matrices to be used to determine the dot arrangement. In this case,
processing of the halftone processing unit is represented by
when C<Th.sub.--C, C.sub.--b=0 (8)
when Th.sub.--C.ltoreq.C, C.sub.--b=1 (9)
when M<Th.sub.--M, M.sub.--b=0 (10)
when Th.sub.--M.ltoreq.M, M.sub.--b=1 (11)
when Y<Th.sub.--Y, Y.sub.--b=0 (12)
when Th.sub.--Y.ltoreq.Y, Y.sub.--b=1 (13)
when K<Th.sub.--K, K.sub.--b=0 (14)
when Th.sub.--K.ltoreq.K, K.sub.--b=1 (15)
[0054] The operation of the image processing unit 13 according to
this embodiment in the output measurement mode is thus
completed.
[0055] Referring back to FIG. 6, processing after image processing
(step S103) will be described. In the output device 2, the
processed image data storage unit 21 acquires and stores the
corrected image data processed by the image processing unit 13
(step S104). The output unit 22 outputs the image data stored in
the processed image data storage unit 21 (step S105).
[0056] In the measuring device 3, the measured data acquisition
unit 31 measures the luminance value of the image output from the
output unit 22, and obtains the measured luminance value as
measured data. In this embodiment, the average luminance of the
image output from the output unit 22 is used as the luminance of
the image. However, any other value that reflects the luminance, in
other words, the color of the image, is usable as the measured
data. In this embodiment, the measured data acquisition unit 31
specifically acquires the image output from the output unit 22 as
measured image data, and calculates the average pixel value of the
measured image data as the average luminance (step S106). In this
embodiment, a digital camera is used as the measuring device.
However, any other measuring device may be used if it can measure
the output with high frequency from the output unit 22 at a
sufficient accuracy.
[0057] FIG. 11 is a schematic view showing the relationship between
reference image data and measured image data. Reference numeral
1601 denotes reference image data having a spatial frequency of 0
[cycle/mm]; 1602, measured image data corresponding to the
reference image data 1601; 1603, reference image data having a
spatial frequency of 11.8 [cycle/mm]; and 1604, measured image data
corresponding to the reference image data 1603.
[0058] IMax and IMin in FIG. 11 represent the maximum value and
minimum value, respectively. The maximum value and minimum value
indicate the maximum value and minimum value of R, G, or B pixel
values. The maximum value and minimum value are independent values
for each of R, G, and B. The measured data acquisition unit 31 may
acquire values for only one component, for example, green, and R
and B may be assumed to have the same values as those of G. The
measured data acquisition unit 31 may acquire the mixed values of
R, G, and B.
[0059] Referring to FIG. 11, Ave and ct represent the average pixel
value and contrast, respectively. The average pixel value and
contrast are given by
Ave=(IMax+IMin)/2 (16)
ct=IMax-IMin (17)
[0060] In this embodiment, the above-described spatial frequency,
spatial frequency direction, average pixel value, and contrast are
employed as the feature amounts.
[0061] Aveout is the average pixel value of the measured image
data, which is calculated like Ave. More specifically, the measured
data acquisition unit 31 obtains the maximum and minimum pixel
values of the measured image data. The measured data acquisition
unit 31 then calculates the average of the maximum and minimum
pixel values as Aveout. Note that the measured data acquisition
unit 31 may calculate, as Aveout, the statistic value of the
measured image data, for example, the weighted average of the pixel
values or the like, and obtain the Aveout as the measured data of
the image output from the output unit 22.
[0062] The target and measured data storing unit 41 stores the
measured data obtained by the measured data acquisition unit 31, in
other words, Aveout which is an average pixel value of the measured
data (step S107). The target and measured data storing unit 41
stores the feature amounts of the reference image data in advance,
or acquires the feature amounts of the reference image data in step
S102. In step S107, the target and measured data storing unit 41
stores a set of the measured data and the feature amounts of the
reference image data. In this embodiment, the target and measured
data storing unit 41 stores a set of the frequency of the reference
image data, the frequency direction of the reference image data,
the average pixel value of the reference image data, the contrast
of the reference image data, and the measured data obtained by
outputting and measuring the reference image data.
[0063] In step S108, the correction parameter calculation unit 42
determines whether all reference image data have been measured. The
reference image data to be measured may be predetermined. The
correction parameter calculation unit 42 may check with the user
whether reference image data to be measured remains. If reference
image data to be measured still remains, the process returns to
step S102 to read out the next reference image data. If all
reference image data have been measured, the process advances to
step S109.
[0064] Subsequently, the correction parameter calculation unit 42
calculates correction parameters H_y and H_x (step S109). The
correction parameter calculation unit 42 uses the target data and
measured data stored in the target and measured data storing unit
41. The correction parameters are calculated so as to minimize the
difference between target data (set value) and the variation
between the average pixel value of reference image data expressed
by a specific frequency (frequency x) and that of reference image
data expressed by another frequency (frequency y).
[0065] First, the correction parameter calculation unit 42 acquires
the set of the feature amount and measured data from the target and
measured data storing unit 41 (set value acquisition). In this
embodiment, the correction parameter calculation unit 42 acquires a
frequency .omega. of the reference image data, the frequency
direction (X or Y) of the reference image data, the average pixel
value Ave of the reference image data, the contrast ct of the
reference image data, and the average pixel value Aveout of the
measured data. When the frequency of the reference image data in
the X direction is .omega., the average pixel value of the
reference image data is Ave, and the contrast of the reference
image data is ct, the average pixel value of measured data is
represented by Aveout_x(.omega., ct, Ave). When the frequency of
the reference image data in the Y direction is .omega., the average
pixel value of the reference image data is Ave, and the contrast of
the reference image data is ct, the average pixel value of measured
data is represented by Aveout_y (.omega., ct, Ave).
[0066] Using these data, the correction parameter calculation unit
42 then obtains the measured reduction rate in the Y direction and
the measured reduction rate in the X direction. They are calculated
by equations (18) and (19). The measured reduction rate is a
numerical value representing a variation between the average pixel
value of reference image data (image x) expressed by a specific
frequency (frequency x) and the average pixel value of reference
image data (image y) expressed by another frequency (frequency y).
In this embodiment, the measured reduction rate represents the
ratio of Aveout for the frequency .omega. to Aveout for the
frequency 0. More specifically, a numerical value obtained by
dividing the average pixel value of the measured data of reference
image data whose feature amount is A by the average pixel value of
the measured data which corresponds to the reference image data
whose frequency is zero and whose feature amount other than
frequency equals to A, is defined as the measured reduction rate
corresponding to the feature amount A. In this embodiment, since
the feature amounts include the frequency direction, the measured
reduction rate is obtained for each of the Y and X directions
by
Rf.sub.--m.sub.--y(.omega.,ct,Ave)=Aveout.sub.--y(.omega.,ct,Ave)/Aveout-
.sub.--y(0,ct,Ave) (18)
Rf.sub.--m.sub.--x(.omega.,ct,Ave)=Aveout.sub.--x(.omega.,ct,Ave)/Aveout-
.sub.--x (0,ct,Ave) (19)
where Rf_m_y and Rf_m_x are the measured reduction rate in the Y
direction and that in the X direction, respectively. That is, the
correction parameter calculation unit 42 calculates the measured
reduction rates Rf_m_y(.omega.,ct,Ave) and Rf_m_x(.omega.,ct,Ave)
for the set of the frequency .omega. of the reference image data,
the contrast ct of the reference image data, and the average pixel
value Ave of the reference image data. When the frequency of the
reference image data in the X direction is .omega., the average
pixel value of the reference image data is Ave, and the contrast of
the reference image data is ct, the measured reduction rate is
represented by Rf_m_x(.omega.,ct,Ave). When the frequency of the
reference image data in the Y direction is .omega., the average
pixel value of the reference image data is Ave, and the contrast of
the reference image data is ct, the measured reduction rate is
represented by Rf_m_y(.omega.,ct,Ave). FIGS. 12A and 12B show the
formats of data actually obtained by the processing. In FIG. 12A,
1701 represents data for the frequency in the Y direction. In FIG.
12B, 1702 represents data for the frequency in the X direction.
[0067] Next, the correction parameter calculation unit 42 acquires
the target data stored in the target and measured data storing unit
41. The target data is a set of the reduction rate in an ideal
output of the reference image data and the feature amounts of the
reference image data. Performing correction of making the measured
image data closer to the target data allows to make the output
image closer to the ideal color.
[0068] If the target of color matching is, for example, a liquid
crystal display, the target data describes "frequency co of
reference image data", "contrast ct of reference image data",
"average pixel value Ave of reference image data", and "target
reduction rate Rf_t" for each of the X and Y directions of the
liquid crystal display. If the target of color reproduction is an
ideal color that is not limited to a device, the target data
describes ideal "frequency co of reference image data", "contrast
ct of reference image data", "average pixel value Ave of reference
image data", and "target reduction rate Rf_t" for each of the X and
Y directions. FIGS. 13A and 13B show the formats of target data.
Note that 1801 in FIG. 13A represents target data in the Y
direction, and 1802 in FIG. 13B represents target data in the X
direction. When the frequency of the reference image data in the X
direction is .omega., the average pixel value of the reference
image data is Ave, and the contrast of the reference image data is
ct, the target reduction rate is represented by
Rf_t_x(.omega.,ct,Ave). When the frequency of the reference image
data in the Y direction is .omega., the average pixel value of the
reference image data is Ave, and the contrast of the reference
image data is ct, the target reduction rate is represented by
Rf_y(.omega.,ct,Ave). Note that the target reduction rate is
calculated like the measured reduction rate. More specifically, an
average luminance (target luminance for Y) in an ideal output
corresponding to the reference image data whose feature amount is A
is acquired. A numerical value obtained by dividing the average
luminance by the average luminance (target luminance for X) of the
ideal output which corresponds to the reference image data, whose
frequency is zero and whose feature amount other than frequency
equals to A is defined as the target reduction rate corresponding
to the feature amount A.
[0069] To correct the image in accordance with the output of a
specific target device, an average observation distance D_t between
the target device output and the observer can be used. An average
observation distance D_m between the observer and the output after
correction using the parameters calculated by the correction
parameter calculation unit 42 will also be used. That is, D_m is
the average observation distance between the observer and the
output in the output correction mode to be described later. The
correction parameter calculation unit 42 acquires D_t and D_m, and
calculates a distance ratio dRatio=D_t/D_m. The correction
parameter calculation unit 42 uses the distance ratio dRatio to
calculate the correction parameters. FIG. 14 is a schematic view
showing the distance ratio dRatio.
[0070] Referring to FIG. 14, reference numeral 1901 denotes an
observer; 1902, an output by a correction target device, in other
words, the output unit 22 or a device of the same type as the
output unit 22; and 1903, an output of a device which is a target
for the color matching. The observation distances D_t and D_m are
not the distances between the output unit and the measuring device
at the time of the above-described measurement. The observation
distances D_t and D_m are the distances between the observer and
the output when observing the outputs from these devices in an
actual application example. The observation distances will be
irrelevant when correcting the image to an ideal color. In this
case, dRatio can be set to 1.
[0071] The correction parameter calculation unit 42 obtains the
correction parameter H_y for the frequency in the Y direction from
the measured reduction rate Rf_m_y and the target reduction rate
Rf_t_y. The correction parameter calculation unit 42 also obtains
the correction parameter H_x for the frequency in the X direction
from the measured reduction rate Rf_m_x and the target reduction
rate Rf_t_x. The correction parameters H_y and H_x are calculated
by
H.sub.--y(.omega.,ct,Ave)=(Rf.sub.--t.sub.--y(.omega..times.dRatio,ct,Av-
e)/Rf.sub.--m.sub.--y(.omega.,ct,Ave)) (20)
H.sub.--x(.omega.,ct,Ave)=(Rf.sub.--t.sub.--x(.omega..times.dRatio,ct,Av-
e)/Rf.sub.--m.sub.--x(.omega.,ct,Ave)) (21)
[0072] That is, a numerical value obtained by dividing the target
reduction rate by the measured reduction rate is acquired as a
correction parameter. When the frequency of the reference image
data in the X direction is .omega., the average pixel value of the
reference image data is Ave, and the contrast of the reference
image data is ct, the correction parameter is represented by
H_x(.omega.,ct,Ave). When the frequency of the reference image data
in the Y direction is .omega., the average pixel value of the
reference image data is Ave, and the contrast of the reference
image data is ct, the correction parameter is represented by
H_y(.omega.,ct,Ave).
[0073] In equations (20) and (21), the frequency .omega. that is an
argument of the target data Rf_t_y and Rf_t_x is multiplied by
dRatio. This is done because the distance between the target for
the color matching and the correction target device is different.
Then, even if the two outputs have the same frequency, the
frequency of the output from the target visible for the observer is
dRatio times higher than the output of the correction target device
visible for the observer. In other words, this multiplication makes
the frequency scales of the two outputs match.
[0074] To calculate the correction parameters H_y and H_x for the
frequency .omega., the target data Rf_t_y and Rf_t_x for the
frequency .omega..times.dRatio are used. However, the target and
measured data storing unit 41 may not store the target data for the
frequency .omega..times.dRatio. In this case, using an estimation
method such as linear interpolation based on Rf_t_y and Rf_t_x for
an adjacent frequency enables to calculate the target data for the
frequency .omega..times.dRatio. More specifically, target data for
the frequency y can be obtained by linear interpolation using
target data (set value y1) for an image y1 with a frequency y1 and
target data (set value y2) for an image y2 with a frequency y2.
FIGS. 15A and 15B show the correction parameters calculated by the
correction parameter calculation unit 42. Note that 2001 in FIG.
15A indicates correction parameters for the frequency in the Y
direction, and 2002 in FIG. 15B indicates correction parameters for
the frequency in the X direction.
[0075] Finally, the correction parameter calculation unit 42
transfers the calculated correction parameters to the correction
parameter-storage unit 14 of the image correction device 1 (step
S110).
[0076] The processing in the "output measurement mode" thus ends.
With the processing described above, the correction
parameter-storage unit 14 stores the correction parameters, and
image color correction comes to be ready.
[0077] [Output Correction]
[0078] The output correction mode in which an image is corrected
using correction parameters and output will be described below. The
"output correction mode" of the image processing apparatus
according to this embodiment will be described with reference to
the flowchart of FIG. 16. First, the output mode setting unit 11
acquires a user instruction, and sets the output mode to the output
correction mode (step S301).
[0079] Next, the image data acquisition unit 12 acquires and stores
an input image as image data (step S302). In the output correction
mode, an arbitrary image can be acquired as image data. The image
processing unit 13 processes the image data stored in the image
data acquisition unit 12 (step S303). A detailed operation of the
image processing unit 13 according to this embodiment in the output
correction mode will be described below with reference to the block
diagram of FIG. 8 and the flowchart of FIG. 17.
[0080] The output mode setting unit 11 sets the switching unit 131
based on the setting acquired from the user (step S401). If the
output mode setting unit 11 instructs the output correction mode,
the color conversion unit 132 acquires image data via the feature
amount calculation unit 138 and the correction unit 139. The
feature amount calculation unit 138 calculates the feature amounts
near a pixel of interest using a plurality of spatial filters for
it. The pixel of interest is one pixel of the input image
represented by the image data stored in the image data acquisition
unit 12. The feature amount calculation unit 138 repeatedly
performs the calculation, thereby calculating the feature amounts
of each pixel of the input image (step S402). In this embodiment,
the spatial frequency, contrast, and average pixel value are
obtained as the feature amounts.
[0081] In this embodiment, for example, six sets of spatial filters
are defined for each of the X and Y directions to calculate the
spatial frequency and contrast. In addition, one set of spatial
filters is set for each of the X and Y directions to calculate the
average pixel value. The number of filters may change. In this
embodiment, one-dimensional filters in the X or Y direction are
defined. However, one-dimensional filters in other directions, for
example, oblique directions may be defined. Alternatively,
two-dimensional filters may be defined.
[0082] When the spatial filters to be used to calculate the spatial
frequency and contrast of the embodiment are defined as F_X(n) and
F_Y(n) (1.ltoreq.n.ltoreq.6), equations to be used to calculate the
spatial frequency and contrast are given by
R.sub.--c(n)=R*F.sub.--X(n) (22)
G.sub.--c(n)=G*F.sub.--X(n) (23)
B.sub.--c(n)=B*F.sub.--X(n) (24)
R.sub.--c(n+6)=R*F.sub.--Y(n) (25)
G.sub.--c(n+6)=G*F.sub.--Y(n) (26)
B.sub.--c(n+6)=B*F.sub.--Y(n) (27)
(1.ltoreq.n.ltoreq.6, and * indicates convolution) Note that adding
6 to the argument on the left-hand side of each of equations (25)
to (27) will be described later.
[0083] FIGS. 18A to 18D show two sets of representative filters out
of the spatial filters to be used to calculate the spatial
frequency and contrast feature amounts according to the embodiment.
FIG. 19 shows the spatial frequency characteristics of the six sets
of spatial filters according to the embodiment. Filters having
other numerical values and other spatial frequency characteristics
may be used, as a matter of course.
[0084] A filter 3101 in FIG. 18A is a filter F_X(1) to be used to
extract the lowest spatial frequency and contrast in the X
direction. In this embodiment, the filter 3101 corresponds to a
spatial frequency of 0.369 cycle/mm, in other words, 2301 in FIG.
19. A filter 3102 in FIG. 18B is a filter F_X(6) to be used to
extract the highest spatial frequency and contrast in the X
direction, and corresponds to 2306 in FIG. 19. The six sets of
filters used in this embodiment correspond to frequencies of
0.369.times.Power(2,n-1) (1.ltoreq.n.ltoreq.6), respectively.
[0085] Similarly, a filter 3103 in FIG. 18C is a filter F_Y(1) to
be used to extract the lowest spatial frequency, in other words,
the spatial frequency of 0.369 cycle/mm and contrast in the Y
direction. A filter 3104 in FIG. 18D is a filter F_Y(6) to be used
to extract the highest spatial frequency and contrast in the Y
direction.
[0086] When the spatial filters to be used to calculate the average
pixel value of this embodiment are defined as Fa_X and Fa_Y,
equations to be used to calculate the average pixel value are given
by
R_aveX=R*Fa.sub.--X (28)
G_aveX=G*Fa.sub.--X (29)
B_aveX=B*Fa.sub.--X (30)
R_aveY=R*Fa.sub.--Y (31)
G_aveY=G*Fa.sub.--Y (32)
B_aveY=B*Fa.sub.--Y (33)
(* indicates convolution)
[0087] FIGS. 4A and 4B show spatial filters to be used to calculate
the average pixel value according to the embodiment. A filter 2201
in FIG. 4A is a filter Fa_X to be used to extract the average pixel
value in the X direction. A filter 2202 in FIG. 4B is a filter Fa_Y
to be used to extract the average pixel value in the Y
direction.
[0088] The correction unit 139 selects a correction parameter
stored in the correction parameter-storage unit 14, which
corresponds to the feature amount calculated by the feature amount
calculation unit 138 for the pixel of interest (parameter
acquisition). The pixel of interest is one pixel of the image
represented by the image data. The correction unit 139 calculates
the pixel value after correction (corrected pixel value) using the
selected correction parameter and the value of the pixel of
interest. The correction unit 139 performs this processing for each
pixel of the image represented by the image data, thereby acquiring
corrected image data (step S403).
[0089] To select the correction parameter, the above-described
feature amounts R_c(n), G_c(n), B_c(n), R_c(n+6), G_c(n+6),
B_c(n+6), R_aveX, G_aveX, B_aveX, R_aveY, G_aveY, and B_aveY are
used. In this embodiment, correction parameter selection for G out
of R, G, and B will be explained. For R and B, the same processing
as that for G may be executed. Alternatively, the correction
parameter calculated for G may be applied to R and B.
[0090] First, the correction unit 139 obtains a value m that
maximizes G_c(m), thereby determining a frequency feature amount
M_g given by
M.sub.--g=Max.sub.--m(G.sub.--c(m)) (1.ltoreq.m.ltoreq.12) (34)
A function Max m(G c(m)) of equation (34) returns m that maximizes
G_c(m). The range of m is 1.ltoreq.m.ltoreq.12 because the feature
amounts in the X and Y directions are calculated, as indicated by
equations (23) and (26).
[0091] The above-described frequency M g does not represent the
actual spatial frequency. Hence, M_g needs to be converted into an
actual spatial frequency M_fg. In this embodiment, the actual
spatial frequency M_fg is calculated in accordance with the
following equations. Note that 0.369 [cycle/mm] is the center
frequency of the filter corresponding to the lowest frequency in
the six sets of filters, as shown in FIG. 19.
When 1.ltoreq.M.sub.--g.ltoreq.6,
M.sub.--fg=0.369.times.Power(2,M.sub.--g-1) (35)
When 7.ltoreq.M.sub.--g12,
M.sub.--fg=0.369.times.Power(2,M.sub.--g-7) (36)
[0092] Next, the correction unit 139 calculates the maximum value
of G_c(m), thereby determining a contrast feature amount M_cg given
by
M.sub.--cg=Max.sub.--G.sub.--c(G.sub.--c(m)) (1.ltoreq.m.ltoreq.12)
(37)
A function Max_G_c(G_c(m)) on the right-hand side of equation (37)
returns the maximum value of G_c(m).
[0093] In addition, the correction unit 139 determines whether to
obtain the average pixel value in the X direction or the average
pixel value in the Y direction from M_g represented by equation
(34), thereby determining an average pixel value Ave_mg
corresponding to the direction. For example, the average pixel
value Ave_mg can be determined as follows.
When 1.ltoreq.M.sub.--g.ltoreq.6, Ave.sub.--mg=G_aveX (38)
When 7.ltoreq.M.sub.--g.ltoreq.12, Ave.sub.--mg=G_aveY (39)
[0094] Using the frequency feature amount M_fg, contrast feature
amount M_cg, and average pixel value Ave_mg obtained by the
above-described calculations, the correction unit 139 selects a
target correction parameter H_pg from the parameters H_x and H_y
held in the correction parameter-storage unit 14. More
specifically, the correction parameter can be selected in the
following way.
When 1.ltoreq.M.sub.--g.ltoreq.6,
H.sub.--pg=H.sub.--x(M.sub.--fg,M.sub.--cg,Ave.sub.--mg) (40)
When 7.ltoreq.M.sub.--g.ltoreq.12,
H.sub.--pg=H.sub.--y(M.sub.--fg,M.sub.--cg, Ave.sub.--mg) (41)
The correction unit 139 corrects the pixel value based on the
selected correction parameter. At the time of selection, the
correction parameter-storage unit 14 does not always have H_x or
H_y having the frequency M_fg, contrast M_cg, and average pixel
value Ave_mg as arguments. In this case, the correction unit 139
replaces the frequency M_fg, contrast M_cg, and average pixel value
Ave_mg with the closest values prepared in the correction
parameter-storage unit 14 in advance, and uses the replaced values
as the arguments. The correction unit 139 then obtains the
correction parameters H_x and H_y. As another method, the
correction unit 139 may linearly interpolate the frequency .omega.,
contrast ct, average pixel value Ave, and correction parameters H_x
and H_y held in the correction parameter-storage unit 14 in
advance. The correction unit 139 may thus obtain the correction
parameters H_x and H_y corresponding to the frequency M_fg,
contrast M_cg, and average pixel value Ave_mg.
[0095] The correction processing is represented by
G.sub.--p=G.times.H.sub.--pg (42)
[0096] Correction of G out of R, G, and B is thus completed. As
described above, correcting R and B as well enables to generate
corrected signals R_p, G_p, and B_p. In some cases, the correction
parameter-storage unit 14 may not have the correction parameters
corresponding to the frequency feature amount M_fg, contrast
feature amount M_cg, and average pixel value Ave_mg. Then, the
correction unit 139 may estimate the correction parameters
corresponding to the feature amounts using acquirable correction
parameters. For example, the correction unit 139 may acquire a
correction parameter by interpolating a plurality of correction
parameters corresponding to the frequency feature amount M_fg.
[0097] In this embodiment, the feature amount calculation unit 138
and the correction unit 139 calculate feature amounts near each of
the pixels of the image represented by the image data and perform
correction. However, only the pixel values of pixels within a
designated region may be corrected. It is also possible to
calculate feature amounts in a designated region of an image, and
correct the pixels in the designated region using one parameter
obtained by the feature amount calculation.
[0098] Next, the color conversion unit 132 converts the corrected
image data R_p, G_p, and B_p into R', G', and B' data by looking up
the color conversion LUT 133 (step S404).
R'=R_LUT.sub.--3D(R.sub.--p,G.sub.--p,B.sub.--p) (43)
G'=G_LUT.sub.--3D(R.sub.--p,G.sub.--p,B.sub.--p) (44)
B'=B_LUT.sub.--3D(R.sub.--p,G.sub.--p,B.sub.--p) (45)
[0099] The functions on the right-hand sides of equations (43) to
(45) correspond to the contents of the color conversion LUT
133.
[0100] Color conversion of this embodiment is thus completed. Color
separation (step S405) and halftone processing (step S406) are the
same as in steps S203 and S204, respectively, and a detailed
description thereof will not be repeated. The operation of the
image processing unit 13 according to this embodiment in the output
correction mode is thus completed. Note that in this embodiment,
the correction target signals are R, G, and B. Needless to say, the
correction may be done for CMYK data obtained by color
separation.
[0101] The processed image data storage unit 21 acquires and stores
the image data generated by the processing of the image processing
unit 13 (step S304). The display/output unit 22 outputs the image
data stored in the processed image data storage unit 21 (step
S305). Processing in the "output correction mode" thus ends. With
the above-described processing, an image obtained by causing the
image processing unit 13 to correct, using a correction parameter
held in the correction parameter-storage unit 14, image data
acquired by the image data acquisition unit 12 is output.
[0102] As described above, according to this embodiment, color
correction of a pixel of interest is performed using color
correction parameters corresponding to the spatial frequency,
spatial frequency direction, and pixel value distribution near the
pixel of interest, thereby improving the accuracy of color matching
for a high-resolution image. It is consequently possible to correct
and output the color of a high-resolution image to be output from a
given device such that it matches with the color to be output from
another device. It is also possible to correct the color of a
high-resolution image to be output from a device to an ideal color.
In this embodiment, the spatial frequency, frequency direction,
contrast, and average pixel value are used as the feature amounts.
However, other feature amounts such as the mode, median, minimum
value, maximum value, and the like of a pixel value may be
used.
Second Embodiment
[0103] In the first embodiment, the units included in the devices
shown in FIG. 5 are implemented as hardware. However, the units may
be implemented as software. In this case, the software is held in
various kinds of storage devices provided in a computer. A CPU
executes the software so that the computer can implement the
functions of the units shown in FIG. 5.
[0104] FIG. 3 is a block diagram showing the hardware configuration
of a computer applicable to this embodiment. A computer 2101 is a
general-purpose information processing apparatus such as a popular
personal computer. Blocks to be described later are connected via a
bus 2107 in the computer 2101. The bus 2107 enables transfer of
various kinds of data between the blocks.
[0105] Note that the apparatus to which the computer 2101 is
applied does not always need all constituent elements shown in FIG.
3. In that case, some of the constituent elements shown in FIG. 3
can be omitted. Alternatively, some of the constituent elements
shown in FIG. 3 may be replaced with hardware having the same
function. The computer 2101 may be formed from a plurality of
computers.
[0106] Referring to FIG. 3, a CPU 2102 controls the entire computer
2101 using programs and data loaded to a main storage device 2103.
The CPU 2102 also executes the above-described processing to be
performed by the image processing apparatus to which the computer
2101 is applied. The main storage device 2103 is represented by a
RAM. The main storage device 2103 has an area to temporarily store
programs and data read out from various storage devices. The
storage devices include an HDD (Hard Disk Drive) 2104, CD drive
2109, DVD drive 2110, and FDD (Floppy.degree. Disk Drive) 2111. The
main storage device 2103 also has an area to temporarily store
image data acquired from a scanner 2117 via an I/F (interface)
2115. The main storage device 2103 also has a work area to be used
by the CPU 2102 to execute various kinds of processing. That is,
the main storage device 2103 can provide various information
recording areas as needed.
[0107] The HDD 2104 stores the OS (Operating System) and various
kinds of images (including document images). The HDD 2104 also
stores programs and data to be used by the CPU 2102 to control the
functions of the units shown in FIG. 3 or execute the
above-described processing which is performed by the apparatus to
which the computer 2101 is applied. The programs and data stored in
the HDD 2104 are loaded to the main storage device 2103 as needed
under the control of the CPU 2102 and processed by the CPU 2102.
Note that several pieces of information described to be stored in
the main storage device 2103 may be stored in the HDD 2104.
[0108] A video controller 2105 sends, to a monitor 2106, display
data including image data and character data received from the main
storage device 2103 or HDD 2104 as signals. The monitor 2106 is
formed from a CRT, liquid crystal panel, or the like. The monitor
2106 displays an image, characters, or the like based on the
signals received from the video controller 2105.
[0109] An I/F 2108 connects a printer 2116 to the computer 2101.
The computer 2101 can transmit print data to the printer 2116 via
the I/F 2108, and receive the status information of the printer
2116 transmitted from the printer 2116. The CD drive 2109 reads out
programs and data recorded on a CD serving as a storage medium, and
sends the readout programs and data to the HDD 2104, main storage
device 2103, or the like.
[0110] The DVD drive 2110 reads out programs and data recorded on a
DVD serving as a storage medium, and sends the readout programs and
data to the HDD 2104, main storage device 2103, or the like. The
FDD 2111 reads out programs and data recorded on a floppy.RTM. disk
serving as a storage medium, and sends the readout programs and
data to the HDD 2104, main storage device 2103, or the like.
[0111] Reference numerals 2113 and 2114 denote a mouse and a
keyboard, respectively, each of which serves as an operation input
device. The user of the computer 2101 can input various
instructions to the CPU 2102 by operating the mouse 2113 or
keyboard 2114. An I/F 2112 connects the keyboard 2114 and the mouse
2113 to the bus. An operation instruction input by the user using
the mouse 2113 or keyboard 2114 is sent to the CPU 2102 via the I/F
2112 as a signal.
[0112] The I/F 2115 connects the scanner 2117 that generates image
data by reading a document, film, or the like to the computer 2101.
Image data generated by the scanner 2117 is sent to the HDD 2104,
main storage device 2103, or the like via the I/F 2115. An I/F 2118
communicates information with another electronic device such as a
computer. Information acquired from a network 2119 based on an
instruction of the CPU 2102, which may include image data, is sent
to the HDD 2104, main storage device 2103, or the like via the I/F
2118.
Other Embodiments
[0113] Aspects of the present invention can also be realized by a
computer of a system or apparatus (or devices such as a CPU or MPU)
that reads out and executes a program recorded on a memory device
to perform the functions of the above-described embodiment(s), and
by a method, the steps of which are performed by a computer of a
system or apparatus by, for example, reading out and executing a
program recorded on a memory device to perform the functions of the
above-described embodiment(s). For this purpose, the program is
provided to the computer for example via a network or from a
recording medium of various types serving as the memory device (for
example, computer-readable medium).
[0114] While the present invention has been described with
reference to exemplary embodiments, it is to be understood that the
invention is not limited to the disclosed exemplary embodiments.
The scope of the following claims is to be accorded the broadest
interpretation so as to encompass all such modifications and
equivalent structures and functions.
[0115] This application claims the benefit of Japanese Patent
Application No. 2009-124721, filed 22 May 2009, which is hereby
incorporated by reference herein in its entirety.
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